[GitHub] [arrow-datafusion] stuartcarnie commented on a diff in pull request #6617: RFC: User Defined Window Functions

["stuartcarnie (via GitHub)" <gi...@apache.org>]

stuartcarnie commented on code in PR #6617:
URL: https://github.com/apache/arrow-datafusion/pull/6617#discussion_r1227543327


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datafusion-examples/examples/simple_udwf.rs:
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@@ -0,0 +1,210 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+use std::sync::Arc;
+
+use arrow::{
+    array::{AsArray, Float64Array},
+    datatypes::Float64Type,
+};
+use arrow_schema::DataType;
+use datafusion::datasource::file_format::options::CsvReadOptions;
+
+use datafusion::error::Result;
+use datafusion::prelude::*;
+use datafusion_common::DataFusionError;
+use datafusion_expr::{
+    partition_evaluator::PartitionEvaluator, Signature, Volatility, WindowUDF,
+};
+
+// create local execution context with `cars.csv` registered as a table named `cars`
+async fn create_context() -> Result<SessionContext> {
+    // declare a new context. In spark API, this corresponds to a new spark SQLsession
+    let ctx = SessionContext::new();
+
+    // declare a table in memory. In spark API, this corresponds to createDataFrame(...).
+    println!("pwd: {}", std::env::current_dir().unwrap().display());
+    let csv_path = format!("datafusion/core/tests/data/cars.csv");
+    let read_options = CsvReadOptions::default().has_header(true);
+
+    ctx.register_csv("cars", &csv_path, read_options).await?;
+    Ok(ctx)
+}
+
+/// In this example we will declare a user defined window function that computes a moving average and then run it using SQL
+#[tokio::main]
+async fn main() -> Result<()> {
+    let ctx = create_context().await?;
+
+    // register the window function with DataFusion so wecan call it
+    ctx.register_udwf(my_average());
+
+    // Use SQL to run the new window function
+    let df = ctx.sql("SELECT * from cars").await?;
+    // print the results
+    df.show().await?;
+
+    // Use SQL to run the new window function
+    // `PARTITION BY car`:each distinct value of car (red, and green) should be treated separately
+    // `ORDER BY time`: within each group (greed or green) the values will be orderd by time
+    let df = ctx
+        .sql(
+            "SELECT car, \
+                      speed, \
+                      lag(speed, 1) OVER (PARTITION BY car ORDER BY time),\
+                      my_average(speed) OVER (PARTITION BY car ORDER BY time),\
+                      time \
+                      from cars",
+        )
+        .await?;
+    // print the results
+    df.show().await?;
+
+    // // ROWS BETWEEN 2 PRECEDING AND 2 FOLLOWING: Run the window functon so that each invocation only sees 5 rows: the 2 before and 2 after) using
+    // let df = ctx.sql("SELECT car, \
+    //                   speed, \
+    //                   lag(speed, 1) OVER (PARTITION BY car ORDER BY time ROWS BETWEEN 2 PRECEDING AND 2 FOLLOWING),\
+    //                   time \
+    //                   from cars").await?;
+    // // print the results
+    // df.show().await?;
+
+    // todo show how to run dataframe API as well
+
+    Ok(())
+}
+
+// TODO make a helper funciton like `crate_udf` that helps to make these signatures
+
+fn my_average() -> WindowUDF {
+    WindowUDF {
+        name: String::from("my_average"),
+        // it will take 2 arguments -- the column and the window size
+        signature: Signature::exact(vec![DataType::Int32], Volatility::Immutable),
+        return_type: Arc::new(return_type),
+        partition_evaluator: Arc::new(make_partition_evaluator),
+    }
+}
+
+/// Compute the return type of the function given the argument types
+fn return_type(arg_types: &[DataType]) -> Result<Arc<DataType>> {
+    if arg_types.len() != 1 {
+        return Err(DataFusionError::Plan(format!(
+            "my_udwf expects 1 argument, got {}: {:?}",
+            arg_types.len(),
+            arg_types
+        )));
+    }
+    Ok(Arc::new(arg_types[0].clone()))
+}
+
+/// Create a partition evaluator for this argument
+fn make_partition_evaluator() -> Result<Box<dyn PartitionEvaluator>> {
+    Ok(Box::new(MyPartitionEvaluator::new()))
+}

Review Comment:
   Is it possible we could support passing scalar arguments when creating an instance of the function, similar to the built-in functions?
   
   For example, the `lag` function takes an optional scalar value for the second argument, which is the shift offset:
   
   https://github.com/apache/arrow-datafusion/blob/a42cc8d98b6e875c485e7e9b106d30803a32b00a/datafusion/core/src/physical_plan/windows/mod.rs#L148-L152



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